Loose End 1

In this lrm() fit, what do R2(20,1000) and R2(20,567) mean?

• See https://hbiostat.org/bib/r2.html.

Loose End 2

What can you do when you’re fitting a model to predict a multi-categorical outcome with highly unbalanced categories?

Options include

• discriminant analysis,
• k-nearest neighbors, and
• Random Forest Classifiers among other things

Loose End 3

What can you do if you don’t believe the proportional hazards assumption in a time-to-event analysis?

• Introduction to Survival Analysis in R from UCLA gives examples of the two most common strategies: stratify by the non-PH variable and add an interaction of the non-PH variable with time to the model
• Survival Analysis with R by Joseph Rickert (2017) links to several nice tutorials.
• Methods for non-proportional hazards in clinical trials: A systematic review by Bardo et al. 2024 is a good summary.

Loose End 4

Gelman A (2008) Scaling regression inputs by dividing by two standard deviations (PDF)

Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation…. The resulting coefficients are then directly comparable for untransformed binary predictors.

Loose End 5

What do you do to transform data that aren’t tamed well by a power transformation?

• Yeo-Johnson transformation can handle both positive and negative outcome values, unlike Box-Cox.
• Median regression (or, more generally, quantile regression) is less sensitive to outliers and non-normality, as is using robust standard errors.
• Use bootstrap methods to estimate standard errors.
• The bestNormalize package in R.

Loose End 6

How do I incorporate something other than a weakly informative prior in stan_glm() when I fit a Bayesian regression?

• See these vignettes for the rstanarm package
• Use tidymodels as demonstrated in its learning tools or in Tidy Modeling with R by Kuhn and Silge.

Loose End 7

How do I ingest an ASCII file into R, like [these .dat files associated with the YRBS]https://www.cdc.gov/yrbs/data/index.html)?

• Often all you need is read_delim(), which works like read_csv() and which is part of the readr package in the core tidyverse.
• Check out the Data import with the tidyverse cheat sheet
• In fact, many of the Posit Cheat Sheets are worth a look.

Loose End 8

Dr. Love, thanks for the information about the tidyverse, but I need to code something in base R now for some horrible reason. Is there a place I can go to get an idea of what’s happening there?

• R for Data Science has a chapter called A field guide to base R which might be useful to you.
• This dplyr vignette compared dplyr functions to their base R equivalents.
• This article by David Robinson formed a lot of my thinking around why I teach the tidyverse.

On Fitting Regression Models

Some things are always true…

• Being honest about your findings is important.
• Identifying a stable phenomenon to study is crucial.
• Measuring that phenomenon well is crucial.
• Being transparent about your work is important. Describing your work so it can be replicated, and then actually replicating it, are good things. Sharing your data and your materials is a good idea.

On Fitting Regression Models

Some things are always true…

• Having a large sample size (n) is helpful in fitting models.

but the impact of lots of other things changes depending on why you’re fitting a regression model.

The Key Point

Decide what your research question is, and use it to help you think about what’s important in your modeling.

• Models that account for only a few of the possibly important dimensions of a problem don’t lead to causal conclusions, but can help screen out important from less important areas for future work.
• Model development strategies that work well with large sample sizes (n) and small numbers of predictors to consider don’t necessarily work well when the situation is reversed.

The Key Point

Decide what your research question is, and use it to help you think about what’s important in your modeling.

• Most problems involve missing data, and problems in measurement. Getting those issues settled effectively is often overlooked.
• The sample size you need to fit a regression model changes depending on your aims.

Leek JT and Peng RD “What is the Question” Science 2015-03-20

On Fitting Models

What are some of the reasons you might fit a linear (or generalized linear) model?

1. All prediction, no explanations
2. All description, external validity is irrelevant
3. Clinical/Scientific Prediction with strong priors
4. Above all else, simplicity
5. Causal inference
6. Risk Adjustment

On Fitting Models

What are some of the reasons you might fit a linear (or generalized linear) model?

1. (All prediction, no explanations) Because you want to make predictions about an outcome in new data based on some training data you have, but you’re happy to take those predictions as emerging from a mysterious magic “black box” that cannot be peered into without spoiling the surprise. You don’t care if your results are a little biased, so long as the predictions are strong. Parsimony doesn’t matter to you.

On Fitting Models

1. (All prediction, no explanations)

• Some especially useful tools here include: variable (feature) selection through cross-validation, stepwise approaches, AIC and BIC, machine learning tools like regression trees, and other means of quickly searching through many possible models.
• Sample size is rarely a big issue here. The big problem is having more variables than you can possibly plot at once. You usually have enough data to partition into separate development and test samples.

On Fitting Models

2. (All description, external validity is irrelevant) Because you want to describe, as accurately as possible, the nature of the associations you observe in the available data, but you don’t care much at all whether the conclusions you draw will hold up in new data.

• Confidence intervals for coefficients (slopes, mostly), and sometimes you’ll run the model on clinically relevant cutpoints rather than continuous predictors to see what’s happening more simply.

On Fitting Models

2. (All description, external validity is irrelevant)

• Simple polynomial models can be appealing, and you’ll sometimes want to build this in the ANCOVA context, where you’re looking for the impact of specific pre-specified interactions.
• Residual plots play a big role here in deciding whether the model “fits” well enough, or identifying cases when it doesn’t.
• Cross-validation is useful, but not a big part of convincing people that the model is “right” or not.

On Fitting Models

3. (Clinical prediction) You want to do an excellent job predicting an outcome in new data, but you have a lot of prior knowledge about the predictors under consideration, and want to use that information to help produce prediction rules as effectively as possible. You welcome the fact that most relationships are non-linear, but would like to be parsimonious if possible, as data are often expensive.

On Fitting Models

3. (Clinical prediction)

• Some especially useful tools here include: scatterplot matrices (when the number of predictors is modest), cross-validation, assessments of discrimination and calibration, Spearman’s \(\rho^2\) plot to point the way to non-linear terms that might be impactful if present, restricted cubic splines, polynomial functions, and graphical tools like nomograms
• Most stepwise tools aren’t helpful here. We try to not “peek” at the outcome-predictor relationships to maintain unbiased estimates of the relationship without extensive validation.

On Fitting Models

4. (Above all else, simplicity) You want the problem to look like one in a statistics textbook, where everything is fit with the simplest possible model, where every term adds statistically significant predictive value, and where obtaining an unbiased estimate of the outcome is especially important. You still care a bit about what happens in new data, but you’re mostly concerned about parsimonious model development.

On Fitting Models

4. (Above all else, simplicity)

• Some especially useful tools include stepwise and related approaches, and methods for pruning a set of predictors with clustering or principal components analysis. These models usually make the (often incorrect) assumption that relationships are linear.

On Fitting Models

4. (Above all else, simplicity)

• Often this approach is used by people who are trying to pre-specify their entire model in advance, and want to be sure they can “explain” the result when they are done. That may not be a reasonable thing to hope for. It is important to set expectations in advance appropriately.

On Fitting Models

5. (Causal inference) You want to identify whether a particular causal pathway you have pre-specified matches up well with what you see in new data.
6. (Risk Adjustment) You want to identify the impact of a particular exposure/predictor on an outcome, while controlling for the effects of a series of additional predictors. Perhaps you’ve done a randomized experiment / clinical trial, and want to identify whether particular results meet a standard for statistical (as well as clinical) significance. Power is very important.

On Fitting Models

5. (Causal inference) or 6. (Risk Adjustment)

• Bias is very important, and you want to avoid it. Careful design of a comparison group (like I teach in 500) is a very good way to go about this work, but it’s also true that there’s a lot of epidemiology that goes into drawing causal conclusions, or even thinking hard about an association.
• Often the details of modeling take a back seat to the details of designing the study (and the comparison groups.)

from @EpiEllie (Dr. Ellie Murray)

Well, draw good pictures

From Andrew Gelman 2023-04-23

Hey - here’s some ridiculous evolutionary psychology for you, along with some really bad data analysis.

Jonathan Falk writes:

So I just started reading The Mind Club, which came to me highly recommended. I’m only in chapter 2. But look at this graph…

An Example (from Cook) (part 1/3)

Suppose I want to know what percentage of artists are left handed and I survey 400 artists. I find that 127 of artists surveyed were southpaws. A 95% confidence interval, using the most common approach results in a confidence interval of (0.272, 0.363).

This comes from:

\[ \hat{p} \pm Z_{\alpha/2} \sqrt{\frac{\hat{p}(1 - \hat{p})}{n}} \]

An Example (from Cook) (part 2/3)

Suppose we redo our analysis using a Bayesian approach. Say we start with a uniform prior on \(\theta\).

• Then the posterior distribution on \(\theta\) will have a beta(128, 264) distribution.

Looking at the density function, we can then say in clear conscience that there is a 94% posterior probability that \(\theta\) is in the interval (0.272, 0.363).

Some potential objections (part 3/3)

There are a couple predictable objections at this point. First, we didn’t get exactly 95%. No, we didn’t. But we got very close.

Second, the posterior probability depends on the prior probability. However, it doesn’t depend much on the prior.

Suppose you said “I’m pretty sure most people are right handed, maybe 9 out of 10, so I’m going to start with a beta(1, 9) prior.” If so, you would compute the probability of \(\theta\) being in the interval (0.272, 0.373) to be 0.948.

Can you have confidence in a confidence interval?

Often frequentist and Bayesian analyses reach approximately the same conclusions. A Bayesian can view frequentist techniques as convenient ways to produce approximately correct Bayesian results. And a frequentist can justify using a Bayesian procedure because the procedure has good frequentist properties.

• https://www.johndcook.com/blog/2023/04/23/confidence-interval/

Frank Harrell’s Reaction

Frank Harrell’s Reaction

Frank Harrell’s Reaction

George Cobb on Assessment (2004)

http://www.rossmanchance.com/artist/proceedings/cobb.pdf

(My remarks) address three concerns: fairness, grade inflation, and a third concern that for now I’ll simply label “Roger.” Each of the three concerns is linked to a corresponding attitude toward assessment.

What was I doing in your Project B presentation?

1. I was trying to systematically pay attention to you.

2. I was trying to emphasize things you’ve done well and things you can fix.

(from Gary Larson)

What was I doing?

1. I was trying to systematically pay attention to you.

2. I was trying to emphasize things you’ve done well and things you can fix.

3. I was trying to make it safe to screw up.

4. And I was abandoning fairness in favor of assessing your work in your context.

Want to impress me?

Instead of telling me what the value of a coefficient means in generic terms:

A one-unit increase in X is associated with an increase in Y of blah blah blah.

Talk about the impact of your predictors on your outcome using the actual context of the problem you’re studying. Be specific, not generic, to be more effective.

Embrace Variation!

Show us graphs and tables that help us better understand how much we should “know” after your work about the relationships you observe in the data, and describe those things in terms of the actual problem under study.

Statistics / Data Science is…

… a science, not a branch of mathematics, but uses mathematical models as essential tools.

• John Tukey

Statistics is an important tool in the data analysis/science toolbox. Statistics provides a coherent framework for thinking about random variation, and tools to partition data into signal and noise.

• Hadley Wickham

Statistics / Data Science is …

… more than just p values and how you get to them.

In fact, forget about null hypothesis significance testing entirely and concentrate instead on:

• embracing variation,
• exploring data and building models and
• studying the size of effects more meaningfully,

even in the rare and unfortunate case where an important and binary decision “must” be made.

Statistics / Data Science is …

… too important to be left to statisticians.